In this paper, we propose a new method to estimate the packet loss probability of the MMPP/D/1 queuing system by Importance Sampling (IS). In order to estimate rare event we do not increase the arrival rate of traffic, but we decrease service rate of queuing packet. In [5], the authors also proposed an online estimation for the tail probability of FIFO queue length. However, the authors used arrival process is a Poisson process, it is simpler than MMPP arrival process in our method. Finally, we implement our algorithm and compare accuracy and simulation time of our experiments to the Monte Carlo method (MC) and conventional IS method.
{"title":"Online Estimation for Packet Loss Probability of MMPP/D/1 Queuing by Importance Sampling","authors":"Hung Nguyen Ngoc, K. Nakagawa","doi":"10.1145/3287921.3287928","DOIUrl":"https://doi.org/10.1145/3287921.3287928","url":null,"abstract":"In this paper, we propose a new method to estimate the packet loss probability of the MMPP/D/1 queuing system by Importance Sampling (IS). In order to estimate rare event we do not increase the arrival rate of traffic, but we decrease service rate of queuing packet. In [5], the authors also proposed an online estimation for the tail probability of FIFO queue length. However, the authors used arrival process is a Poisson process, it is simpler than MMPP arrival process in our method. Finally, we implement our algorithm and compare accuracy and simulation time of our experiments to the Monte Carlo method (MC) and conventional IS method.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124366996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a method for automated user interface testing of Windows-based applications to increase the accuracy in identifying the target widgets or executing several interactions. The key idea of this method is to generate new test scenarios from widgets and test specification where widgets are extracted during the execution of the application and test specification is generated by combining the interactions of widgets. Furthermore, the paper contributes some techniques to detect hidden widgets which considering as one of the most challenging problems in user interface testing. Currently, a supporting tool has been implemented and tested with several industrial projects. The details of the experimental results will be presented and discussed.
{"title":"A method for Automated User Interface Testing of Windows-based Applications","authors":"D. Tran, Pham Ngoc Hung, Tung Nguyen Duy","doi":"10.1145/3287921.3287939","DOIUrl":"https://doi.org/10.1145/3287921.3287939","url":null,"abstract":"This paper proposes a method for automated user interface testing of Windows-based applications to increase the accuracy in identifying the target widgets or executing several interactions. The key idea of this method is to generate new test scenarios from widgets and test specification where widgets are extracted during the execution of the application and test specification is generated by combining the interactions of widgets. Furthermore, the paper contributes some techniques to detect hidden widgets which considering as one of the most challenging problems in user interface testing. Currently, a supporting tool has been implemented and tested with several industrial projects. The details of the experimental results will be presented and discussed.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122031655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dang Hoang Long, Minh-Tien Nguyen, Ngo Xuan Bach, Le-Minh Nguyen, Tu Minh Phuong
This paper introduces a scoring method to improve the quality of content selection in an extractive summarization system. Different from previous models mainly using local information inside sentences such as sentence position or sentence length, our method judges the importance of a sentence based on its own information and the relation between sentences. For the relation between sentences, we utilize textual entailment, a relationship indicating that the meaning of a sentence can be inferred from another one. Unlike previous work on using textual entailment for summarization, we go a step further by looking at aligned words in an entailment sentence pair. Assuming that important words in a salient sentence can be aligned by several words in other sentences, word alignment scores are exploited to compute the entailment score of a sentence. To take advantage of local and neighbor information for facilitating the salient estimation of sentences, we combine entailment scores with sentence position scores. We validate the proposed scoring method with greedy or integer linear programming approaches for extracting summaries. Experiments on three datasets (including DUC 2001 and 2002) in two different domains show that our model obtains competitive ROUGE-scores with state-of-the-art methods for single-document summarization.
{"title":"An Entailment-based Scoring Method for Content Selection in Document Summarization","authors":"Dang Hoang Long, Minh-Tien Nguyen, Ngo Xuan Bach, Le-Minh Nguyen, Tu Minh Phuong","doi":"10.1145/3287921.3287976","DOIUrl":"https://doi.org/10.1145/3287921.3287976","url":null,"abstract":"This paper introduces a scoring method to improve the quality of content selection in an extractive summarization system. Different from previous models mainly using local information inside sentences such as sentence position or sentence length, our method judges the importance of a sentence based on its own information and the relation between sentences. For the relation between sentences, we utilize textual entailment, a relationship indicating that the meaning of a sentence can be inferred from another one. Unlike previous work on using textual entailment for summarization, we go a step further by looking at aligned words in an entailment sentence pair. Assuming that important words in a salient sentence can be aligned by several words in other sentences, word alignment scores are exploited to compute the entailment score of a sentence. To take advantage of local and neighbor information for facilitating the salient estimation of sentences, we combine entailment scores with sentence position scores. We validate the proposed scoring method with greedy or integer linear programming approaches for extracting summaries. Experiments on three datasets (including DUC 2001 and 2002) in two different domains show that our model obtains competitive ROUGE-scores with state-of-the-art methods for single-document summarization.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132444766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dimension reduction is to find a low-dimensional subspace to project high-dimensional data on, such that the discriminative property of the original higher-dimensional data is preserved. In supervised dimension reduction, class labels are integrated into the lower-dimensional representation, to produce better results on classification tasks. The supervised dimension reduction (SDR) framework by [17] is one of the state-of-the-art methods that takes into account not only the class labels but also the neighborhood graphs of the data, and have some advantages in preserving the within-class local structure and widening the between-class margin. However, the reduced-dimensional representation produced by the SDR framework suffers from the class overlapping problem - in which, data points lie closer to a different class rather than the class they belong to. The class overlapping problem can hurt the quality on the classification task. In this paper, we propose a new method to reduce the overlap for the SDR framework in [17]. The experimental results show that our method reduces the size of the overlapping set by an order of magnitude. As a result, our method outperforms the pre-existing framework on the classification task significantly. Moreover, visualization plots show that the reduced-dimensional representation learned by our method is more scattered for within-class data and more separated for between-class data, as compared to the pre-existing SDR framework.
{"title":"Reducing Class Overlapping in Supervised Dimension Reduction","authors":"N. T. Tung, V. Dieu, Khoat Than, Ngo Van Linh","doi":"10.1145/3287921.3287925","DOIUrl":"https://doi.org/10.1145/3287921.3287925","url":null,"abstract":"Dimension reduction is to find a low-dimensional subspace to project high-dimensional data on, such that the discriminative property of the original higher-dimensional data is preserved. In supervised dimension reduction, class labels are integrated into the lower-dimensional representation, to produce better results on classification tasks. The supervised dimension reduction (SDR) framework by [17] is one of the state-of-the-art methods that takes into account not only the class labels but also the neighborhood graphs of the data, and have some advantages in preserving the within-class local structure and widening the between-class margin. However, the reduced-dimensional representation produced by the SDR framework suffers from the class overlapping problem - in which, data points lie closer to a different class rather than the class they belong to. The class overlapping problem can hurt the quality on the classification task. In this paper, we propose a new method to reduce the overlap for the SDR framework in [17]. The experimental results show that our method reduces the size of the overlapping set by an order of magnitude. As a result, our method outperforms the pre-existing framework on the classification task significantly. Moreover, visualization plots show that the reduced-dimensional representation learned by our method is more scattered for within-class data and more separated for between-class data, as compared to the pre-existing SDR framework.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126995393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Website defacement attacks have been one of major threats to websites and web portals of private and public organizations. The attacks can cause serious consequences to website owners, including interrupting the website operations and damaging the owner's reputation, which may lead to big financial losses. A number of techniques have been proposed for website defacement monitoring and detection, such as checksum comparison, diff comparison, DOM tree analysis and complex algorithms. However, some of them only work on static web pages and the others require extensive computational resources. In this paper, we propose a machine learning-based method for website defacement detection. In our method, machine learning techniques are used to build classifiers (detection profile) for page classification into either Normal or Attacked class. As the detection profile can be learned from training data, our method can work well for both static and dynamic web pages. Experimental results show that our approach achieves high detection accuracy of over 93% and low false positive rate of less than 1%. In addition, our method does not require extensive computational resources, so it is practical for online deployment.
{"title":"A Website Defacement Detection Method Based on Machine Learning Techniques","authors":"Xuan Dau Hoang","doi":"10.1145/3287921.3287975","DOIUrl":"https://doi.org/10.1145/3287921.3287975","url":null,"abstract":"Website defacement attacks have been one of major threats to websites and web portals of private and public organizations. The attacks can cause serious consequences to website owners, including interrupting the website operations and damaging the owner's reputation, which may lead to big financial losses. A number of techniques have been proposed for website defacement monitoring and detection, such as checksum comparison, diff comparison, DOM tree analysis and complex algorithms. However, some of them only work on static web pages and the others require extensive computational resources. In this paper, we propose a machine learning-based method for website defacement detection. In our method, machine learning techniques are used to build classifiers (detection profile) for page classification into either Normal or Attacked class. As the detection profile can be learned from training data, our method can work well for both static and dynamic web pages. Experimental results show that our approach achieves high detection accuracy of over 93% and low false positive rate of less than 1%. In addition, our method does not require extensive computational resources, so it is practical for online deployment.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132309357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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